Dataset Information

Mechanophenotyping of 3D multicellular clusters using displacement arrays of rendered tractions.

ABSTRACT: Epithelial tissues mechanically deform the surrounding extracellular matrix during embryonic development, wound repair, and tumor invasion. Ex vivo measurements of such multicellular tractions within three-dimensional (3D) biomaterials could elucidate collective dissemination during disease progression and enable preclinical testing of targeted antimigration therapies. However, past 3D traction measurements have been low throughput due to the challenges of imaging and analyzing information-rich 3D material deformations. Here, we demonstrate a method to profile multicellular clusters in a 96-well-plate format based on spatially heterogeneous contractile, protrusive, and circumferential tractions. As a case study, we profile multicellular clusters across varying states of the epithelial-mesenchymal transition, revealing a successive loss of protrusive and circumferential tractions, as well as the formation of localized contractile tractions with elongated cluster morphologies. These cluster phenotypes were biochemically perturbed by using drugs, biasing toward traction signatures of different epithelial or mesenchymal states. This higher-throughput analysis is promising to systematically interrogate and perturb aberrant mechanobiology, which could be utilized with human-patient samples to guide personalized therapies.

SUBMITTER: Leggett SE

PROVIDER: S-EPMC7084145 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Mechanophenotyping of 3D multicellular clusters using displacement arrays of rendered tractions.

Leggett Susan E SE Patel Mohak M Valentin Thomas M TM Gamboa Lena L Khoo Amanda S AS Williams Evelyn Kendall EK Franck Christian C Wong Ian Y IY

Proceedings of the National Academy of Sciences of the United States of America 20200302 11

Epithelial tissues mechanically deform the surrounding extracellular matrix during embryonic development, wound repair, and tumor invasion. Ex vivo measurements of such multicellular tractions within three-dimensional (3D) biomaterials could elucidate collective dissemination during disease progression and enable preclinical testing of targeted antimigration therapies. However, past 3D traction measurements have been low throughput due to the challenges of imaging and analyzing information-rich ...[more]

PMID: 32123100

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Project description:Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models.We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture.We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community.

Dataset Information

Mechanophenotyping of 3D multicellular clusters using displacement arrays of rendered tractions.

Publications

Mechanophenotyping of 3D multicellular clusters using displacement arrays of rendered tractions.

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